A Practical Digital Signal Carrier Acquisition based on Kalman Filter Theory
Bibliographic record
Abstract
Traditionally Extended Kalman Filter has been proposed and studied for the acquisition of a digital carrier signal. However, this technique imposes a heavy load on the processor. And high performances cannot be expected for the poor linearity, in most cases. To overcome these problems, this paper shows that one can use a simple, purely linear Kalman Filter which consists of two states variables - phase and frequency. This new technique selects the carrier phase as the input to the filter, instead of a pair of orthogonal signal amplitudes. The filtering logic is made up of only 4 additions and 2 multiplications. The results of both simulations and experiments show that this filter can acquire the carrier signal within 10 symbols with a probability of 98 % during the initial phase, even when the frequency offset is as large as 20 % of the symbol rate frequency at C/N=6dB. In the steady state, the measured BER is close to the theoretical values. While delivering a similar performance mentioned above, this filter can operate even when the carrier frequency deviates from the expected figure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".